Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA.
Department of Biological Sciences, The University of Memphis, Memphis, TN, 38152, USA.
J Mol Graph Model. 2023 Jun;121:108429. doi: 10.1016/j.jmgm.2023.108429. Epub 2023 Feb 11.
Pharmacophores are three-dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental testing. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand-based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure-based pharmacophores (which only require an experimentally determined or modeled structure for a protein target) have gained more attention to aid in virtual screening efforts as the number of publicly available experimentally determined GPCR structures have increased (140 unique GPCR represented as of October 24, 2022). Thus, the goal of this study was to develop a method of structure-based pharmacophore model generation applicable to ligand discovery for GPCR that have few known ligands. Pharmacophore models were generated within the active sites of 8 class A GPCR crystal structures via automated annotation of 5 randomly selected functional group fragments to sample diverse combinations of pharmacophore features. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness-of-hit metrics to assess performance. Application of this method to the set of 8 class A GPCR produced pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases), indicating that generated pharmacophore models can prove useful in the context of virtual screening.
药效团是生物活性所必需的分子特征的三维排列，常用于虚拟筛选工作，以优先考虑用于实验测试的配体。G 蛋白偶联受体 (GPCR) 是具有相当兴趣的整合膜蛋白，是配体发现和药物开发的靶标。可以构建基于配体的药效团模型，以确定包括 GPCR 在内的靶标已知生物活性配体之间的结构共性。然而，基于结构的药效团（仅需要蛋白质靶标的实验确定或建模结构）引起了更多关注，以帮助虚拟筛选工作，因为公开提供的实验确定的 GPCR 结构数量增加（截至 2022 年 10 月 24 日，代表 140 个独特的 GPCR）。因此，本研究的目的是开发一种适用于具有少数已知配体的 GPCR 配体发现的基于结构的药效团模型生成方法。通过自动注释 5 个随机选择的功能基团片段，在 8 个 A 类 GPCR 晶体结构的活性部位生成药效团模型，以采样不同的药效团特征组合。然后，使用生成的 5000 个药效团中的每一个搜索包含活性和诱饵/非活性化合物的数据库，针对 30 个 A 类 GPCR 进行评分，并使用富集因子和命中良好度指标进行评估，以评估性能。将该方法应用于 8 个 A 类 GPCR 集合，产生了在已解析结构（8 个中的 8 个）和同源模型（8 个中的 7 个）中都具有理论最大富集因子值的药效团模型，表明生成的药效团模型在虚拟筛选中可能有用。
